The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
There is a growing interest in utilizing unmanned aerial vehicles (UAVs), such as remotely controlled drones/airplanes, helicopters, and multicopters, to perform a wide variety of tasks. An ongoing challenge, though, is how to better control the UAVs for each of these particular uses.
In some applications, it is desirable or useful to perform a task through the use of two or more UAVs that are controlled in a centralized or organized manner. For example, swarm control may be used to control the UAVs as they fly over a targeted geographical area. A swarm can be thought of as a self-organizing particle system with numerous autonomous, reflexive agents (e.g., UAVs are the particles) whose collective movements may be determined by local influences, such as wind and obstacles (such as another nearby UAV). The UAVs are independent and are often locally controlled, which may include communicating with a nearby UAV to determine which one moves or whether both should move to avoid an impending collision.
Formations of multi-agent systems can be controlled based on two main approaches—namely: centralized and decentralized. The centralized approach has the merit of global information that each agent can receive through a central controller. In the decentralized approach, each agent has a local controller which increases its reliability. A decentralized approach can also be useful when global information is not available.
In the context of flight management, synchronization is agreement in time or simultaneous operation, which is an important concept in the control of dynamical systems. An important technique to improve synchronization performance is cross coupling, which is based on sharing the feedback information of control loops, and it has many applications in the motion synchronization of multi-axis and cooperative manipulator robots. On the one hand, coupling with more agents provides a better motion synchronization; on the other hand, communication range of agents restricts the agents with whom an agent can be coupled.
The use of UAVs for achieving high-speed wireless communications is expected to play an important role in future communication systems. In a radio access network (RAN), UAVs equipped with RAN transceivers can be used to quickly deploy RAN services and provide reliable broadband network infrastructure with lower CAPEX and OPEX compared to terrestrial cellular networks. For example, such UAVs can function as relays between a ground-based base transceiver station (BTS) and one or more user equipments (UEs).
The high mobility of UAVs can result in highly dynamic network topologies, which are usually sparsely and intermittently connected. As a result, effective multi-UAV coordination, or UAV swarm operations, should be designed to ensure reliable network connectivity.
Another challenge stems from the size, weight, and power constraints of UAVs, which limit their communication, computation, and endurance capabilities. To address such issues, energy-aware UAV deployment and operation mechanisms are needed for intelligent energy usage and replenishment. Moreover, due to the mobility of UAVs, as well as the lack of fixed backhual links, interference coordination among neighboring cells with UAV-enabled aerial base stations is more challenging than in terrestrial cellular systems. While conventional wisdom might call for effective interference management techniques for UAV-aided cellular coverage, Cooperative Multiple-Input Multiple Output (MIMO) has been shown to exploit interference to provide dramatic improvements to RAN performance.
In 2001, Shattil introduced Coordinated Multipoint (U.S. Prov. Appl. No. 60/286,850) with joint MIMO processing between cooperating BTSs. This enables universal frequency reuse whereby the full RAN spectrum can be used to serve each and every UE. In 2002, Shattil introduced Cloud-RAN (U.S. Pat. No. 7,430,257) wherein Software Defined Radio (SDR) is implemented via distributed computing in a Coordinated Multipoint system. In 2002 and 2004, Shattil described client-side Cooperative MIMO (U.S. Pat Pub. 20080095121 and U.S. Pat. No. 8,670,390). By employing Cooperative MIMO at both ends of a RAN link, the total data bandwidth per UE is no longer limited by the RAN spectrum. Rather, it is determined by the short-range, high-bandwidth fronthaul network that connects cooperating devices. This solves one of the most important problems in radio communications. Each of the references mentioned herein is incorporated by reference in its entirety.
In order for UAVs to serve in a Cooperative-MIMO network, for example, the UAVs could be coordinated spatially to ensure and/or enhance RAN performance. UAV control for RAN applications could include providing for communications between UAVs, providing for synchronization, cluster formation, as well as other control and/or sensing capabilities which are also essential for swarm management. Hence, there is a need to enable a behavioral structure for swarm management configured for multi-objective missions that include RAN performance enhancement, and can further comprise flight management, target seeking, obstacle avoidance, as well as others.